Created on Skills Network Labs

ReadFile-4-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Example1.txt', <http.client.HTTPMessage at 0x7f1104df4cf8>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "41 # Download data\n",
    "import urllib.request\n",
    "url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/labs/example1.txt'\n",
    "filename = 'Example1.txt'\n",
    "urllib.request.urlretrieve(url, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-03-04 11:34:52--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/labs/example1.txt\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 45 [text/plain]\n",
      "Saving to: ‘/resources/data/Example1.txt’\n",
      "\n",
      "/resources/data/Exa 100%[===================>]      45  --.-KB/s    in 0s      \n",
      "\n",
      "2021-03-04 11:34:52 (12.6 MB/s) - ‘/resources/data/Example1.txt’ saved [45/45]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "42 # Download Example file\n",
    "\n",
    "\n",
    "!wget -O /resources/data/Example1.txt https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/labs/example1.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "43 # Read the Example1.txt\n",
    "\n",
    "example1 = \"Example1.txt\"\n",
    "file1 = open(example1, \"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Example1.txt'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "44 # Print the path of file\n",
    "\n",
    "file1.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'r'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "45 # Print the mode of file, either 'r' or 'w'\n",
    "\n",
    "file1.mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "46 # Read the file\n",
    "\n",
    "FileContent = file1.read()\n",
    "FileContent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "47 # Print the file with '\\n' as a new line\n",
    "\n",
    "print(FileContent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "48 # Type of file content\n",
    "\n",
    "type(FileContent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "49 # Close file after finish\n",
    "\n",
    "file1.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is line 1 \n",
      "This is line 2\n",
      "This is line 3\n"
     ]
    }
   ],
   "source": [
    "50 # Open file using with\n",
    "\n",
    "with open(example1, \"r\") as file1:\n",
    "    FileContent = file1.read()\n",
    "    print(FileContent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "51 # Verify if the file is closed\n",
    "\n",
    "file1.closed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "52 # See the content of file\n",
    "\n",
    "print(FileContent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This\n"
     ]
    }
   ],
   "source": [
    "52 # Read first four characters\n",
    "\n",
    "with open(example1, \"r\") as file1:\n",
    "    print(file1.read(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This\n",
      " is \n",
      "line 1 \n",
      "\n",
      "This is line 2\n"
     ]
    }
   ],
   "source": [
    "53 # Read certain amount of characters\n",
    "\n",
    "with open(example1, \"r\") as file1:\n",
    "    print(file1.read(4))\n",
    "    print(file1.read(4))\n",
    "    print(file1.read(7))\n",
    "    print(file1.read(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is line 1 \n",
      "\n",
      "This \n",
      "is line 2\n"
     ]
    }
   ],
   "source": [
    "54 # Read certain amount of characters\n",
    "\n",
    "with open(example1, \"r\") as file1:\n",
    "    print(file1.read(16))\n",
    "    print(file1.read(5))\n",
    "    print(file1.read(9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first line: This is line 1 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "55 # Read one line\n",
    "\n",
    "with open(example1, \"r\") as file1:\n",
    "    print(\"first line: \" + file1.readline())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0 :  This is line 1 \n",
      "\n",
      "Iteration 1 :  This is line 2\n",
      "\n",
      "Iteration 2 :  This is line 3\n"
     ]
    }
   ],
   "source": [
    "56 # Iterate through the lines\n",
    "\n",
    "with open(example1,\"r\") as file1:\n",
    "        i = 0;\n",
    "        for line in file1:\n",
    "            print(\"Iteration\", str(i), \": \", line)\n",
    "            i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "57 # Read all lines and save as a list\n",
    "\n",
    "with open(example1, \"r\") as file1:\n",
    "    FileasList = file1.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This is line 1 \\n'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "58 # Print the first line\n",
    "\n",
    "FileasList[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "59 # Print the third line\n",
    "\n",
    "FileasList[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-03-04 12:29:36--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%204/ex4.csv\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 718 [text/csv]\n",
      "Saving to: ‘ex4.csv’\n",
      "\n",
      "ex4.csv             100%[===================>]     718  --.-KB/s    in 0.001s  \n",
      "\n",
      "2021-03-04 12:29:36 (759 KB/s) - ‘ex4.csv’ saved [718/718]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "61 #Exercise Download the file \n",
    "\n",
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%204/ex4.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-03-04 12:30:54--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%204/ex4.csv\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 718 [text/csv]\n",
      "Saving to: ‘ex4.csv.1’\n",
      "\n",
      "ex4.csv.1           100%[===================>]     718  --.-KB/s    in 0.001s  \n",
      "\n",
      "2021-03-04 12:30:55 (999 KB/s) - ‘ex4.csv.1’ saved [718/718]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "62 ##Download the file \n",
    "\n",
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%204/ex4.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "statData =\"ex4.csv\"\n",
    "\n",
    "def getNAvg(file,N):\n",
    "    \"\"\"\n",
    "    file - File containting all the raw weather station data\n",
    "    N - The number of days to compute the moving average over\n",
    "\n",
    "    Return a list of containg the moving average of all data points\n",
    "    \"\"\"\n",
    "    row = 0 # keep track of rows\n",
    "    lastN = [] # keep track of last N points\n",
    "    mean = [0] # running avg\n",
    "\n",
    "\n",
    "    with open(file,\"r\") as rawData: \n",
    "        for line in rawData:\n",
    "            if (row == 0): # Ignore the headers\n",
    "                row = row + 1\n",
    "                continue\n",
    "\n",
    "            line = line.strip('\\n')\n",
    "            lineData = float(line.split(',')[1])\n",
    "\n",
    "            if (row<=N): \n",
    "                lastN.append(lineData)\n",
    "                mean[0] = (lineData + mean[0]*(row-1))/row\n",
    "            else:\n",
    "                mean.append( mean[row - N -1]+ (lineData - lastN[0])/N)\n",
    "                lastN = lastN[1:]\n",
    "                lastN.append(lineData)\n",
    "\n",
    "            row = row +1            \n",
    "        return mean\n",
    "\n",
    "def plotData(mean,N):\n",
    "        \"\"\" Plots running averages \"\"\"\n",
    "        mean = [round(x,3) for x in mean]\n",
    "        plt.plot(mean,label=str(N) + ' day average')\n",
    "        plt.xlabel('Day')\n",
    "        plt.ylabel('Precipiation')\n",
    "        plt.legend()\n",
    "\n",
    "\n",
    "\n",
    "plotData(getNAvg(statData,1),1)\n",
    "plotData ([0 for x in range(1,5)]+ getNAvg(statData,5),5 )\n",
    "plotData([0 for x in range(1,7)] + getNAvg(statData,7),7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getNAvg : \n",
      "Test Passed\n"
     ]
    }
   ],
   "source": [
    "avg5 =[4.18,4.78,4.34,4.72,5.48,5.84,6.84,6.76,6.74,5.46,4.18,2.74,2.52,2.02,2.16,2.82,2.92,4.36,4.74,5.12,5.34,6.4,6.56,6.1,5.74,5.62,4.26]\n",
    "avg7 =[4.043,4.757,5.071,5.629,6.343,5.886,6.157,5.871,5.243,4.386,3.514,2.714,2.586,2.443,2.571,3.643,4.143,4.443,4.814,5.6,6.314,6.414,5.429,5.443,4.986]\n",
    "\n",
    "def testMsg(passed):\n",
    "    if passed:\n",
    "       return 'Test Passed'\n",
    "    else :\n",
    "       return ' Test Failed'\n",
    "\n",
    "print(\"getNAvg : \")\n",
    "try:\n",
    "    sol5 = getNAvg(statData,5)\n",
    "    sol7 = getNAvg(statData,7)\n",
    "    \n",
    "    if(len(sol5)==len( avg5) and (len(sol7)==len(avg7))):\n",
    "        err5 = sum([abs(avg5[index] - sol5[index])for index in range(len(avg5))])\n",
    "        err7 = sum([abs(avg7[index] - sol7[index])for index in range(len(avg7))])\n",
    "        print(testMsg((err5 < 1) and (err7 <1)))\n",
    "        \n",
    "    else:\n",
    "        print(testMsg(False))\n",
    "except NameError as e: \n",
    "    print('Error! Code: {c}, Message: {m}'.format(c = type(e).__name__, m = str(e)))\n",
    "except:\n",
    "    print(\"An error occured. Recheck your function\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}